ML based approach for inverting penetration depth of SAR signals over large desert areas

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成果类型：

期刊论文

作者：

Zhu, Jun;Liu, Guanxin;Zhao, Rong;Ding, Xiaoli;Fu, Haiqiang

通讯作者：

Liu, GX

作者机构：

[Zhu, Jun] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Dept Surveying Engn, Changsha, Peoples R China.

[Ding, Xiaoli; Liu, Guanxin] Hong Kong Polytech Univ, Fac Construct & Environm, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China.

[Zhao, Rong] Cent South Univ Forestry & Technol, Sch Civil Engn, Dept Surveying Engn, Changsha, Peoples R China.

[Fu, Haiqiang] Cent South Univ, Sch Geosci & Info Phys, Dept Geomatics Sci & Technol, Changsha, Peoples R China.

通讯机构：

[Liu, GX ] H

Hong Kong Polytech Univ, Fac Construct & Environm, Dept Land Surveying & Geoinformat, Hong Kong, Peoples R China.

语种：

英文

关键词：

Penetration depth;Desert area;Hematite;Kufra Basin;Random forests model

期刊：

Remote Sensing of Environment

ISSN：

0034-4257

年：

2023

卷：

295

页码：

113643

DOI：

10.1016/j.rse.2023.113643

基金类别：

Research Grants Council (RGC) of the Hong Kong Special Administrative Region [PolyU 152164/18E, PolyU 152233/19E]; Research Institute for Sustainable Urban Development (RISUD); Hong Kong Polytechnic University; Innovative Technology Fund [ITP/019/20LP]

机构署名：

本校为其他机构

院系归属：

土木工程学院

摘要：

Penetration depth of synthetic aperture radar (SAR) signals over a desert is a key parameter to understand the internal properties of the desert. Existing approaches for obtaining the penetration depth require good quality interferometric SAR (InSAR) data of very short temporal and long spatial baselines. Such data are often difficult to obtain in a highly dynamic desert. We propose a new machine learning (ML) based approach for inverting penetration depth of SAR signals over large desert areas by jointly using InSAR, polarimetric SAR (PolSAR) and optical remote sensing data. First, SAR scatte...

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ML based approach for inverting penetration depth of SAR signals over large desert areas

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